AI and ML for Decision-makers E-Learning Training 

Begrijp de kracht van kunstmatige intelligentie en machine learning voor strategische besluitvorming.

In een wereld waar data de drijvende kracht is achter innovatie, is het voor managers en besluitvormers essentieel om de fundamenten van AI en ML te begrijpen. Deze training biedt een helder overzicht van hoe kunstmatige intelligentie en machine learning worden ingezet om gedrag te voorspellen, patronen te herkennen en besluitvorming te ondersteunen binnen uiteenlopende sectoren.

Je leert wat AI en ML zijn, hoe voorspellende algoritmen werken, en hoe je deze technologieën praktisch kunt toepassen om je organisatie datagedreven te maken — zonder dat je zelf hoeft te programmeren.

Waarom kiezen voor deze opleiding?

• Begrijp hoe AI en ML bedrijfswaarde creëren
• Leer patronen herkennen en voorspellingen interpreteren
• Ontvang praktische inzichten in hoe deze technologieën jouw beslissingen ondersteunen
• Ontwikkel een gemeenschappelijke taal met je data- en IT-teams
• Inclusief 365 dagen toegang tot leerbronnen, labs, mentor en assessments

Wie zou moeten deelnemen?

Deze training is ideaal voor:

• Managers en besluitvormers die betrokken zijn bij datagedreven strategieën
• Leiders die AI/ML willen begrijpen zonder technische voorkennis
• Innovatiemanagers en strategen binnen digitale transformaties
• Consultants die AI/ML-oplossingen willen duiden voor klanten

Deze Learning Kit met meer dan 6:10 leeruren is verdeeld in drie sporen:

Demo AI and ML for Decision-makers E-Learning Training 

Cursusinhoud

Track 1: Fundamentals of AI and ML

Courses (1½ hour +):

Fundamentals of AI & ML: Foundational Data Science Methods

Course: 34 Minutes

• Course Overview
• Machine Learning (ML)
• Clustering
• Evaluation of Clustering Algorithm Accuracy
• Classification
• Evaluation of Classification Model Accuracy
• Regression
• Simple Linear Regression
• Multiple Linear Regression
• Machine Learning Challenges
• Course Summary

Fundamentals of AI & ML: Advanced Data Science Methods

Course: 27 Minutes

• Course Overview
• Text Mining
• Evaluate Text Mining Accuracy
• Graph Analysis
• Anomaly Detection
• Novelty Detection
• Association Rule Mining
• Neural Networks
• Course Summary

Fundamentals of AI & ML: Introduction to Artificial Intelligence

Course: 42 Minutes

• Course Overview
• What is Artificial Intelligence (AI)?
• What Can AI Do?
• How AI Works: Data
• How AI Works: Tools and Technologies
• Artificial Intelligence Life Cycle
• Data Science Process: Ask
• Data Science Process: Research
• Data Science Process: Model, Validate, and Test
• Data Science Process: Interpret
• Course Summary

Track 2: Developing an AI/ML Data Strategy

Courses (1½ hours +)

Developing an AI/ML Data Strategy: The Data Analytics Maturity Model

Course: 39 Minutes

• Course Overview
• Data Analytics Maturity Model6
• Analytics Maturity Model Categories
• Data Governance
• Emerging Trends in Data Analytics
• Data Storage Tools
• Data Cleaning and Analysis Tools
• Data Collaboration and Visualization Tools
• Data Tool Selection
• Course Summary

Developing an AI/ML Data Strategy: Building an AI-powered Workforce

Course: 29 Minutes

• Course Overview
• Artificial Intelligence (AI) in the Workforce
• Data Science Team Structures
• Hiring vs. Contracting vs. Training Data Teams
• Data Team Roles
• Data-driven Culture and AI Strategy
• Move toward a Data-driven Culture
• Course Summary

Developing an AI/ML Data Strategy: Data Analytics & Data Ethics

Course: 38 Minutes

• Course Overview
• Data Science Ethics
• Considerations for Adopting AI/ML
• Data Bias Creation, Types, and Reduction
• Key Principles of Data Ethics
• Artificial Intelligence (AI) and Data Ethics
• Data Ethics Examples
• Course Summary

Track 3: Visualizing Data for Impact

Courses (1½ hours +)

Visualizing Data for Impact: Introduction to Data Visualization

Course: 27 Minutes

• Course Overview
• Examples and Use Cases of Data Visualization
• Data Visualization Charts and Graphs Part 1
• Data Visualization Charts and Graphs Part 1
• Know Your Audience
• Choose the Right Visualization
• Select Visualization Tools
• Course Summary

Visualizing Data for Impact: Visual Design Theory

Course: 27 Minutes

• Course Overview
• Visual Design
• Use Contrast and Position
• Size and Group Items
• Add Legends, Arrange Items, and Address Gaps
• Color Usage in Visualizations
• Course Summary

Visualizing Data for Impact: Data Storytelling

Course: 28 Minutes

• Course Overview
• What Is Data Storytelling?
• Refine Your Insight
• Tailor to Your Audience
• Outline Around the Insight
• Plot with a Storyboard
• Format a Story for Delivery
• Course Summary

Visualizing Data for Impact: Analyzing Misleading Visualizations

Course: 27 Minutes

• Course Overview
• Common Data Visualization Mistakes
• Issues with Color and Chart Selection
• Misleading Statistics
• Visual Distortions
• Deceiving Graphs
• Data Omission and Misleading Visualizations
• Course Summary

Track 4: Cloud Computing and MLOps in AI/ML

Courses (1½ hours +)

Cloud Computing and MLOps: Cloud and AI

Course: 46 Minutes

• Course Overview
• Cloud Computing in AI
• The Benefits and Challenges of Cloud Computing
• Cloud AI Strategy Implementation
• The Architecture of Cloud Computing
• AI as a Service (AIaaS)
• Data Management and Governance Cloud AI Tools
• AI in Cloud Security
• Key Cloud Technologies for AI
• Future Trends for Cloud Computing and AI
• Course Summary

Cloud Computing and MLOps: Introduction to MLOps

Course: 36 Minutes

• Course Overview
• What Is XOps?
• Version Control
• Types of Version Control
• Version Control Tools
• What Is MLOps?
• Benefits MLOps
• What Is DataOps?
• Benefits and Use Cases of DataOps
• DataOps Pipeline Elements
• The Role of Humans in ML Pipeline Automation
• MLOps Ethical Concerns
• Course Summary

Cloud Computing and MLOps: ML Pipelines

Course: 26 Minutes

• Course Overview
• What Are ML Pipelines and Why Are They Needed?
• Manual Pipelines
• Automated Pipelines
• ML Pipeline Preparation and Build Best Practices
• Development, Staging, and Production Environments
• CI/CD Pipelines
• Use Cases for CI/CD
• Testing ML Pipelines
• ML Pipeline Testing Tools and Frameworks
• Course Summary

Assessment:

• Final Exam: AI and ML for Decision-makers